Identification of overpressures resulting from undercompaction and hydrocarbon generation in shale dominated settings using well log data

2021 ◽  
pp. 1-38
Author(s):  
Longxiang Tang ◽  
Jungang Lu ◽  
Mingyi Yang ◽  
Huaqin Zhang ◽  
Zhenglu Xiao ◽  
...  
Keyword(s):  
Organic Geochemistry ◽  
Ordos Basin ◽  
Well Logs ◽  
Hydrocarbon Generation ◽  
Well Log ◽  
Log Data ◽  
Hydrocarbon Generation Potential ◽  
Hydro Carbon ◽  
Abnormal Variation ◽  
Exploration And Development

Based on the concepts of organic geochemistry and well log characteristics, we discuss the differences between undercompaction overpressure (UCOP) and hydro-carbon-generating pressurization (HGP) from well logs in the Chang 7 Shale, Ordos Basin. The results revealed that shales have better hydrocarbon generation potential than mudstones. We found the UCOP is characterized by significant changes of acoustic traveltime (AC), compensation neutron (CNL), density (DEN) and resistivity (RT) on the well logs. Whereas the HGP causes obvious changes in the AC and RT logs, minor deviations are generally revealed in the CNL and DEN logs. Further stud-ies revealed that while UCOP occurs primarily in thick shale layers of the sedimentary center, HGP is predominant in the thin shales of the sedimentary edge. The reason for the above difference may be attributed to the ubiquity of the shales associated with HGP, with the variation in Well logs caused by the HGP concealed by the abnormal variation of UCOP. These findings thus revealed the distribution and well log features of abnormal overpressure for the different generating mechanisms, thus providing significant guidance for further exploration and development of the overpressured formations.

scholarly journals Bitumen and hydrocarbon generation potential of paleo-reservoirs in the Ordovician, south Ordos Basin

2010 ◽  
Vol 37 (6) ◽  
pp. 668-673 ◽  
Author(s):  
Chunlin Zhang ◽  
Fenjin Sun ◽  
Ruie Liu ◽  
Fudong Zhang ◽  
Hongping Xiao ◽  
...  
Keyword(s):  
Ordos Basin ◽  
Hydrocarbon Generation ◽  
Hydrocarbon Generation Potential

NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR

2021 ◽  
Author(s):  
Ryan Banas ◽  
◽  
Andrew McDonald ◽  
Tegwyn Perkins ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Unsupervised Learning ◽  
Learning Algorithms ◽  
Well Logs ◽  
Quality Data ◽  
Well Log ◽  
Log Data ◽  
Petrophysical Analysis

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.

Transformer-Based Deep Learning Models for Well Log Processing and Quality Control by Modelling Global Dependence of the Complex Sequences

2021 ◽  
Author(s):  
Ashutosh Kumar
Keyword(s):  
Deep Learning ◽  
Moving Average ◽  
Well Logs ◽  
Well Log ◽  
Learning Models ◽  
Log Data ◽  
Deep Wells ◽  
Data Points ◽  
Complex Sequences ◽  
Sequential Models

Abstract A single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data. Sequential models learn geological patterns and petrophysical trends to detect events across depths of well-log data. However, vanishing gradients, exploding gradients and the limits of convolutional filters, limit the diagnosis of ultra-deep wells in complex subsurface information. Vast number of operations required to detect events between two subsurface points at large separation limits them. Transformers-based Models (TbMs) rely on non-sequential modelling that uses self-attention to relate information from different positions in the sequence of well-log, allowing to create an end-to-end, non-sequential, parallel memory network. We use approximately 21 million data points from 21 wells of Volve for the experiment. LSTMs, in addition to auto-regression (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) conventionally models the events in the time-series well-logs. However, complex global dependencies to detect events in heterogeneous subsurface are challenging for these sequence models. In the presented work we begin with one meter depth of data from Volve, an oil-field in the North Sea, and then proceed up to 1000 meters. Initially LSTMs and ARIMA models were acceptable, as depth increased beyond a few 100 meters their diagnosis started underperforming and a new methodology was required. TbMs have already outperformed several models in large sequences modelling for natural language processing tasks, thus they are very promising to model well-log data with very large depth separation. We scale features and labels according to the maximum and minimum value present in the training dataset and then use the sliding window to get training and evaluation data pairs from well-logs. Additional subsurface features were able to encode some information in the conventional sequential models, but the result did not compare significantly with the TbMs. TbMs achieved Root Mean Square Error of 0.27 on scale of (0-1) while diagnosing the depth up to 5000 meters. This is the first paper to show successful application of Transformer-based deep learning models for well-log diagnosis. Presented model uses a self-attention mechanism to learn complex dependencies and non-linear events from the well-log data. Moreover, the experimental setting discussed in the paper will act as a generalized framework for data from ultra-deep wells and their extremely heterogeneous subsurface environment.

scholarly journals Feasibility Study on the Application of Dynamic Elastic Rock Properties from Well Log for Shale Hydrocarbon Development of Brownshale Formation in the Bengkalis Trough, Central Sumatra Basin, Indonesia.

2021 ◽  
Vol 6 (2) ◽  
pp. 81-85
Author(s):  
Ahmad Muraji Suranto ◽  
Aris Buntoro ◽  
Carolus Prasetyadi ◽  
Ricky Adi Wibowo
Keyword(s):  
Young’S Modulus ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Well Logs ◽  
Poisson's Ratio ◽  
Rock Properties ◽  
Well Log ◽  
Log Data ◽  
Core Data ◽  
Elastic Rock

In modeling the hydraulic fracking program for unconventional reservoir shales, information about elasticity rock properties is needed, namely Young's Modulus and Poisson's ratio as the basis for determining the formation depth interval with high brittleness. The elastic rock properties (Young's Modulus and Poisson's ratio) are a geomechanical parameters used to identify rock brittleness using core data (static data) and well log data (dynamic data). A common problem is that the core data is not available as the most reliable data, so well log data is used. The principle of measuring elastic rock properties in the rock mechanics lab is very different from measurements with well logs, where measurements in the lab are in high stresses / strains, low strain rates, and usually drained, while measurements in well logging use the principle of measured downhole by high frequency sonic. vibrations in conditions of very low stresses / strains, High strain rate, and Always undrained. For this reason, it is necessary to convert dynamic to static elastic rock properties (Poisson's ratio and Young's modulus) using empirical equations. The conversion of elastic rock properties (well logs) from dynamic to static using the empirical calculation method shows a significant shift in the value of Young's Modulus and Poisson's ratio, namely a shift from the ductile zone dominance to the dominant brittle zone. The conversion results were validated with the rock mechanical test results from the analog outcrop cores (static) showing that the results were sufficiently correlated based on the distribution range.

scholarly journals Climate-Provenance Effect on the Organic Matter Enrichment of the Chang 9 Source Rocks in the Central Ordos Basin, China

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ling Ma ◽  
Zhihuan Zhang ◽  
Weiqiu Meng
Keyword(s):  
Organic Matter ◽  
Chemical Weathering ◽  
Ordos Basin ◽  
Source Rocks ◽  
Sediment Provenance ◽  
Type I ◽  
Hydrocarbon Generation ◽  
Hydrocarbon Generation Potential ◽  
Primary Producer ◽  
Geochemical Analyses

The Upper Triassic Chang 9 organic-rich sediments have been considered as effective hydrocarbon source rocks for the Mesozoic petroleum system in the Ordos Basin. Previous studies on the Chang 9 member mostly focused on the influence of their paleoproductivity and paleoredox conditions on the organic matter (OM) enrichment, whereas there are few studies on the influence of the paleoclimate condition and sediment provenance on the OM enrichment. In this study, a series of geochemical analyses was performed on the Chang 9 core samples, and their hydrocarbon generation potential, paleoclimate condition, and sediment provenance were assessed to analyze the effect of paleoclimate-provenance on OM enrichment. The Chang 9 source rocks are characterized by high OM abundance, type I−II OM type, and suitable thermal maturity, implying good hydrocarbon generation potential. Based on the C-values and Sr/Cu ratios, the paleoclimate condition of the Chang 9 member was mainly semihumid. In addition, the Th/Co vs. La/Sc diagram and negative δEuN indicate that the Chang 9 sediments were mainly derived from felsic source rocks. Meanwhile, the paleoweathering intensity of the Chang 9 member is moderate based on moderate values of CIA, PIA, and CIW, which corresponds to the semihumid paleoclimate. The relatively humid paleoclimate not only enhances photosynthesis of the primary producer, but also promotes chemical weathering intensity, leading to suitable terrestrial clastic influx to the lacustrine basin, which is beneficial for OM enrichment.

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